{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "#改入matplotlib和seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#在ipython notebook里，需要加入这一行\n",
    "%matplotlib inline\n",
    "#解决中文显示乱码问题\n",
    "from pylab import mpl \n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "#https://blog.csdn.net/u013527419/article/details/69567347"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    50\n",
       "1    50\n",
       "0    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#改入iris数据集\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "import pandas as pd\n",
    "pd.Series(y).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>花颧长度</th>\n",
       "      <th>花颧宽度</th>\n",
       "      <th>花瓣长度</th>\n",
       "      <th>花瓣宽度</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   花颧长度  花颧宽度  花瓣长度  花瓣宽度  label\n",
       "0   5.1   3.5   1.4   0.2      0\n",
       "1   4.9   3.0   1.4   0.2      0\n",
       "2   4.7   3.2   1.3   0.2      0\n",
       "3   4.6   3.1   1.5   0.2      0\n",
       "4   5.0   3.6   1.4   0.2      0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用pandas查看数据\n",
    "datas = pd.DataFrame(X)\n",
    "datas.columns = ['花颧长度','花颧宽度','花瓣长度','花瓣宽度']\n",
    "datas['label'] = y\n",
    "datas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb4a6da0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb4adb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datas.plot(x='花颧长度',y='花颧宽度',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xbd6fe48>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbeb76d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(datas, hue=\"label\", size=5).map(plt.scatter, \"花颧长度\", \"花颧宽度\").add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
